Inversion Spectral Decomposition with Channel Complex Identification

Abstract

Summary One important application of time frequency analysis is the identification of channel complex. However, conventional time frequency analysis methods suffer from low resolution which sometimes leads to relative terrible interpretation results. In this paper, a novel time frequency analysis approach called inversion spectral decomposition (ISD) is applied to solve these problems. ISD is proposed via inversion strategy and has a higher resolution. In the identification of channel complex, ISD provides not only more detailed time frequency content in time frequency amplitude spectrum than conventional time frequency analysis methods, but also additional phase information, which gives another perspective to identify channel complex, thus reducing the uncertainty in interpretation. In the test of 3D synthetic data, both Gabor transform and ISD are applied to generate time slices. Gabor transform only identifies the middle section of channel complex while ISD clearly identifies the whole channel complex with legible details. Furthermore, ISD provides additional phase information, further confirming the reliability in interpretation. Finally, ISD is applied to a 3D real data, and exhibits an outstanding performance.